This paper considers the problem of determining the optimum target values of the quality characteristic of interest Y and the screening limits of a surrogate variable X which is correlated with Y under two-stage screening procedure. In the two-stage screening procedure, X is measured first to decide whether an item should be accepted, rejected or additional observations should be taken. If it is difficult to decide on the result of measured value of X, Y is then observed to classify the undecided items. Assuming that Y and X are jointly normally distributed, a model is constructed which involves selling and reduced prices, production, inspection, and penalty costs. Methods of finding the optimum process mean and the screening limits are presented. A numerical example and analysis of the results are also presented.

In this paper, a design and estimation procedure for the stochastically dependent nonstationary multiple acceptance sampling plans is developed. At first, the rough-cut acceptance and rejection numbers are given as an initial solution from the corresponding sequential sampling plan. A Monte-Carlo algorithm is used to find the acceptance and rejection probabilities of a lot. The conditional probability formula for a sample path is found. The acceptance and rejection probabilities are found when a decision boundary is given. Several decision criteria and the design procedure to select optimal plans are suggested. The formula for measuring performance of these sampling plans is developed. Type I and II error probabilities are also estimated. As a special case, by setting the stage size as 1 in a dependent sampling plan, a sequential sampling plan satisfying type I and II error probabilities is more accurate and a smaller average sample number can be found. In a numerical example, a Polya dependent process is examined. The sampling performances are shown to compare the selection scheme and the effect of the change of the dependency factor.

We consider a scheduling problem arising in a shipyard subassembly welding process. There are four welding robots of gantry type, which perform the welding process for the subassemblies. Because the robots perform the welding operations at the same time, there is a possibility of collision between adjacent robots depending on the welding schedule. In this paper, we propose a heuristic method to find a welding schedule which does minimize the welding completion time while avoiding collision among the robots. The method consists of two phases: assignment and scheduling. In the assignment phase, we assign each welding line to a proper robot. In the scheduling phase, we determine the welding schedules for the robots so that collision is avoided. Computational experiences with the data which reflect the real situation are reported.

The introduction of general purpose machining centers and the information system based on computer network has added a new control problem to the classical job shop control problems: a routing problem. A routing problem is to determine the machine on which a part will be processed. The modern manufacturing systems are given much system status information including the arrival time of the future parts via the computer network for automation. This paper presents and tests the performance of a routing procedure, LARP(Look-Ahead Routing Procedure) which uses look-ahead information on the future arrival of parts in the system. The manufacturing system considered in this paper has multi-stations which consists of general purpose machines and processes parts of different types. The application of LARP under many operating conditions shows that the reduction of part flow time and tardiness from the cases without using this information is up to 8% for flow time and 21% for tardiness. The procedure introduced here can be used for many highly automated systems such as an FMS and a semi-conductor fabrication system for routing where the arrivals of parts in the near future are known.

This paper develops an efficient heuristic algorithm for scheduling workforce level that can accommodate all the requested maintenance jobs. Each job has its own release and due dates as well as man-day requirement, and must be scheduled in a non-interrupted time interval, namely, without preemption. Duration of each job is not fixed, but to be determined within given specific range. The objective is to minimize workforce level to complete all the requested maintenance jobs. We show that the problem can be seen as a variant of the two-dimensional bin-packing problem with some additional constraints. A non-linear mixed integer programming model for the problem is developed, and an efficient heuristic algorithm based on bin-packing algorithms is proposed. In order to evaluate goodness of the solution obtained from the proposed algorithm, a scheme for getting a good lower bound for the optimum solution is presented and analyzed. The computational experiment shows that the proposed algorithm performs quite satisfactorily.

Shoulder joint is the most movable joint in human body with, at least, three degrees of freedom, since there are at least three bones and five joints involved in shoulder movement. Due to the complexity of the shoulder joint and the lack of appropriate anatomical data, modeling of the shoulder joint has been known to be extremely difficult. In many biomechanical models being used, shoulder joint is considered as a fixed point and it is also assumed that the shoulder joint does not noticeably move during the shoulder movement. However, such an assumption is not valid in real applications and causes inaccuracy, especially, in the area of workspace evaluation. The reachable area generated by a human becomes somewhat different from that of current models for those models fail to appropriately reflect the movement of shoulder joint's center of rotation. In this study, the location of the shoulder joint's center of rotation was obtained in relation to the location of humerus, on which a new model for reach envelope generation was developed for workspace evaluation. From the experiments conducted for three subjects, the initial location of the center of rotation was determined for each subject and subsequent changes in the instantaneous center of rotation were drawn as a function of flexion and abduction of the shoulder. Based on the regression analysis, the study suggested a new method for the generation of reach envelope. Comparisons were also made among real reach envelopes obtained from the experiment, the ones from the model, and the ones from the new method suggested in the study. As a result, the prediction errors incurred from the new method were significantly reduced when compared to the ones from the current approach.

Market share is one of the most important measures in the valuation of prospering firm. It plays a role of composite indicator for the competitiveness of firm. So, the understanding of the underlying process of market share is inevitable factor for the econometricians and the business engager. Lately, the Korean Economy has been placed in the control of IMF. This shock will cause a lot of influence over the domestic economy. The idea that the information about the past shock-response experience will do us good for dealing with this kind of economic shocks is not new. Among numerous markets, we pay attention to the durable goods market, especially automobile market. The automobile market has large repercussion effect over the domestic economy on the issue of both national employment and technology integration. We divided the Korean automobile market into three segments: small, medium, and large-sized car, while each proportion of these segments has been changing slowly. We propose a Bayesian approach to detect and forecast structural changes in time series of the market shares in the domestic automobile market, especially for level shifts and drift changes, and compare the empirical results with other existing approaches.

The improvement of automotive seating system, particularly for the driver, has been the subject of intense interest. In this study, the methods for evaluating the seating comfort are investigated. A subjective evaluation has been the general method for evaluating the seating comfort of automotive seat. Therefore, the survey using the roadside interview is conducted. In addition, the subjective evaluation with a questionnaire using the laboratory set-up is investigated. With this subjective evaluation, in order to evaluate the comfort objectively, the body pressure distribution, seat physical characteristics and eletromygram are investigated. These objective evaluation methods are compared with the subjective evaluation. As a result, the body pressure distribution, seat physical characteristics and electromyogram are recommended as the objective technique for the seating comfort evaluation.

A shop floor control system(SFCS) performs the production activities required to fill orders. In order to effectively control these activities, the autonomous agent-based heterarchical shop floor control architecture is adopted where a supervisor does not exist. In this paper, we define functional perspective of the heterarchical shop floor control using planning, scheduling, and execution modules. In particular, we focus on an execution module that can coordinate the planning and scheduling modules and a general execution module that easily can be modified to execute the other equipment. The execution module can be defined informally as a module that downloads and performs a set of scheduled tasks. The execution module is also responsible for identifying and resolving various errors whether they come from hardware or software. The purpose of this research is to identify all the execution activities and solving techniques under the assumptions of the heterarchical control architecture. And we model the execution module in object-oriented modelling technique for generalization. The execution module modeled in object-oriented concept can be adopted to the other execution module easily. This paper also proposes a classification scheme for execution activities of the heterarchical control architecture. Petri-nets are used as a unified framework for modeling and controlling execution activities. For solving the nonexistence of a supervisor, a negotiation-based solution technique is utilized.

The objectives of this study are twofold: (1) to evaluate the cockpit of three Korean air force fighters such as F-4, F-5, and F-16 in an ergonomic perspective and (2) to measure the musculoskeletal discomfort of the fighter pilots. For the study, 369 air force pilots from 7 squadrons were surveyed. The study shows that the cockpit design of F-16 is superior to the others. However, F-4 is the worst among them. Statistical analyses reveal that the seat in the cockpit raised the most complaints, regardless of types of fighter planes. The main problems with the seat included inappropriate designs of the backrest angle, seat cushioning, and parachute harness. Also frequently cited are various control switches, control stick, rudder pedal, and the throttle. That these items lack human integration is found in remote positions and improper dimensions. The implications of these findings are discussed. The self-reported musculoskeletal complaints show that the main discomfort is on the back and neck. Also, the buttocks, shoulders, and the legs/knees are common sites of discomfort. A stepwise regression analysis shows that the back discomfort, is mainly caused by the use of the seat, rudder pedal, control stick, and switches. A Spearman rank correlation coefficient test also reveals that job dissatisfaction of the pilots is related to the complaints with the cockpit and musculoskeletal discomfort. These findings suggest that more comprehensive studies for cockpit design in the aspects of functional anthropometry of Korean pilots are needed to reduce the musculoskeletal discomfort.

This evapaper is toluate the forecasting performance of three neural network(NN) approaches against ARIMA model using the famous time series analysis competition data. The first NN approach is to analyze the second Makridakis (M2) Competition Data using Multilayer Perceptron (MLP) that has been the most popular NN model in time series analysis. Since it is recently known that MLP suffers from bias/variance dilemma, two approaches are suggested in this study. The second approach adopts Cascade Correlation Network (CCN) that was suggested by Fahlman & Lebiere as an alternative to MLP. In the third approach, a time series is separated into two series using Noise Filtering Network (NFN) that utilizes autoassociative memory function of neural network. The forecasts in the decomposition analysis are the sum of two prediction values obtained from modeling each decomposed series, respectively. Among the three NN approaches, Decomposition Analysis shows the best forecasting performance on the M2 Competition Data, and is expected to be a promising tool in analyzing socio-economic time series data because it reduces the effect of noise or outliers that is an impediment to modeling the time series generating process.

The Little's formula, , expresses a fundamental principle of queueing theory: Under very general conditions, the average queue length is equal to the product of the arrival rate and the average waiting time. This useful formula is now well known and frequently applied. In this paper, we demonstrate that the Little's formula has much more power than was previously realized when it is properly decomposed into what we call the microscopic Little's formula. We use the M/G/1 queue with server vacations as an example model to which we apply the microscopic Little's formula. As a result, we obtain a transform-free expression for the queue length distribution. Also, we briefly summarize some previous efforts in the literature to increase the power of the Little's formula.

This paper deals with a priority queueing model in an ATM system. Two types of customers are considered. Type-1 customers have push-put priority over type-2 customers. Type-1 customers can enter the service only when the number of type-2 customers is less than a threshold T. We derive the joint probability of the number of customers in the buffer, the mean waiting time, and the loss probabilities of each type. We also propose an optimal control policy that satisfies a given quality of service.

In this paper, we consider multiple-class queueing systems in which the server starts a set-up as soon as the number of customers in the "start-up class" reaches threshold N. After the set-up the server starts his service. We obtain the Laplace-Stieltjes transform and the mean of the waiting times of each class of customers for FCFS and non-preemptive priority disciplines.